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    Título
    Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems
    Autor
    Fernández-Guisuraga, José Manuel
    Suárez-Seoane, SusanaAutoridad Buleria
    Fernandes, Paulo M.
    Fernández-García, Víctor
    Fernández Manso, AlfonsoAutoridad BuleriaORCID
    Quintano, Carmen
    Calvo Galván, María LeonorAutoridad BuleriaORCID
    Facultad/Centro
    Facultad de Ciencias Biologicas y Ambientales
    Área de conocimiento
    Ecologia
    Título de la revista
    Forest Ecosystems
    Editor
    Springer
    Fecha
    2022-03
    ISSN
    2197-5620
    Abstract
    Background: The characterization of surface and canopy fuel loadings in fire-prone pine ecosystems is critical for understanding fire behavior and anticipating the most harmful ecological effects of fire. Nevertheless, the joint consideration of both overstory and understory strata in burn severity assessments is often dismissed. The aim of this work was to assess the role of total, overstory and understory pre-fire aboveground biomass (AGB), estimated by means of airborne Light Detection and Ranging (LiDAR) and Landsat data, as drivers of burn severity in a megafire occurred in a pine ecosystem dominated by Pinus pinaster Ait. in the western Mediterranean Basin. Results: Total and overstory AGB were more accurately estimated (R2 equal to 0.72 and 0.68, respectively) from LiDAR and spectral data than understory AGB (R2 ¼ 0.26). Density and height percentile LiDAR metrics for several strata were found to be important predictors of AGB. Burn severity responded markedly and non-linearly to total (R2 ¼ 0.60) and overstory (R2 ¼ 0.53) AGB, whereas the relationship with understory AGB was weaker (R2 ¼ 0.21). Nevertheless, the overstory plus understory AGB contribution led to the highest ability to predict burn severity (RMSE ¼ 122.46 in dNBR scale), instead of the joint consideration as total AGB (RMSE ¼ 158.41). Conclusions: This study novelty evaluated the potential of pre-fire AGB, as a vegetation biophysical property derived from LiDAR, spectral and field plot inventory data, for predicting burn severity, separating the contribution of the fuel loads in the understory and overstory strata in Pinus pinaster stands. The evidenced relationships between burn severity and pre-fire AGB distribution in Pinus pinaster stands would allow the implementation of threshold criteria to support decision making in fuel treatments designed to minimize crown fire hazard.
    Materia
    Ecología. Medio ambiente
    Ingeniería agrícola
    Palabras clave
    Aboveground biomass
    Burn severity
    Landsat
    LiDAR
    Pinus pinaster
    Peer review
    SI
    URI
    http://hdl.handle.net/10612/14320
    DOI
    https://doi.org/10.1016/j.fecs.2022.100022
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